CACNA1E Variants Affect Beta Cell Function in Patients with Newly Diagnosed Type 2 Diabetes. The Verona Newly Diagnosed Type 2 Diabetes Study (VNDS) 3

Background: Genetic variability of the major subunit (CACNA1E) of the voltage-dependent Ca 2+ channel Ca V2.3 is associated to risk of type 2 diabetes, insulin resistance and impaired insulin secretion in nondiabetic subjects. The aim of the study was to test whether CACNA1E common variability affects beta cell function and/or insulin sensitivity in patients with newly diagnosed type 2 diabetes. Methodology/Principal Findings: In 595 GAD-negative, drug naïve patients (mean6SD; age: 58.5610.2 yrs; BMI: 29.965 kg/m 2, HbA1c: 7.061.3) with newly diagnosed type 2 diabetes we: 1. genotyped 10 tag SNPs in CACNA1E region reportedly covering,93 % of CACNA1E common variability: rs558994, rs679931, rs2184945, rs10797728, rs3905011, rs12071300, rs175338, rs3753737, rs2253388 and rs4652679; 2. assessed clinical phenotypes, insulin sensitivity by the euglycemic insulin clamp and beta cell function by state-of-art modelling of glucose/C-peptide curves during OGTT. Five CACNA1E tag SNPs (rs10797728, rs175338, rs2184945, rs3905011 and rs4652679) were associated with specific aspects of beta cell function (p,0.0520.01). Both major alleles of rs2184945 and rs3905011 were each (p,0.01 and p,0.005, respectively) associated to reduced proportional control with a demonstrable additive effect (p,0.005). In contrast, only the major allele of rs2253388 was related weakly to more severe insulin resistance (p,0.05). Conclusions/Significance: In patients with newly diagnosed type 2 diabetes CACNA1E common variability is strongl

Investigating the Role of T-Cell Avidity and Killing Efficacy in Relation to Type 1 Diabetes Prediction

During the progression of the clinical onset of Type 1 Diabetes (T1D), high-risk individuals exhibit multiple islet autoantibodies and high-avidity T cells which progressively destroy beta cells causing overt T1D. In particular, novel autoantibodies, such as those against IA-2 epitopes (aa1-577), had a predictive rate of 100% in a 10-year follow up (rapid progressors), unlike conventional autoantibodies that required 15 years of follow up for a 74% predictive rate (slow progressors). The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells. For this purpose, we build a series of mathematical models incorporating first one clone then multiple clones of islet-specific and pathogenic CD8 and/or CD4 T cells, together with B lymphocytes, to investigate the interaction of T-cell avidity with autoantibodies in predicting disease onset. These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects. The model shows that the level and persistence of autoantibodies depends not only on the avidity of T cells, but also on the killing efficacy of these cells. Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies. Such studies may lead to early diagnosis of the disease in high-risk individuals and thus potentially serve as a means of staging patients for clinical trials of preventive or interventional therapies far before disease onset

A local glucose-and oxygen concentration-based insulin secretion model for pancreatic islets

<p>Abstract</p> <p>Background</p> <p>Because insulin is the main regulator of glucose homeostasis, quantitative models describing the dynamics of glucose-induced insulin secretion are of obvious interest. Here, a computational model is introduced that focuses not on organism-level concentrations, but on the quantitative modeling of local, cellular-level glucose-insulin dynamics by incorporating the detailed spatial distribution of the concentrations of interest within isolated avascular pancreatic islets.</p> <p>Methods</p> <p>All nutrient consumption and hormone release rates were assumed to follow Hill-type sigmoid dependences on local concentrations. Insulin secretion rates depend on both the glucose concentration and its time-gradient, resulting in second-and first-phase responses, respectively. Since hypoxia may also be an important limiting factor in avascular islets, oxygen and cell viability considerations were also built in by incorporating and extending our previous islet cell oxygen consumption model. A finite element method (FEM) framework is used to combine reactive rates with mass transport by convection and diffusion as well as fluid-mechanics.</p> <p>Results</p> <p>The model was calibrated using experimental results from dynamic glucose-stimulated insulin release (GSIR) perifusion studies with isolated islets. Further optimization is still needed, but calculated insulin responses to stepwise increments in the incoming glucose concentration are in good agreement with existing experimental insulin release data characterizing glucose and oxygen dependence. The model makes possible the detailed description of the intraislet spatial distributions of insulin, glucose, and oxygen levels. In agreement with recent observations, modeling also suggests that smaller islets perform better when transplanted and/or encapsulated.</p> <p>Conclusions</p> <p>An insulin secretion model was implemented by coupling local consumption and release rates to calculations of the spatial distributions of all species of interest. The resulting glucose-insulin control system fits in the general framework of a sigmoid proportional-integral-derivative controller, a generalized PID controller, more suitable for biological systems, which are always nonlinear due to the maximum response being limited. Because of the general framework of the implementation, simulations can be carried out for arbitrary geometries including cultured, perifused, transplanted, and encapsulated islets.</p

Creating a sustainable federation of cloud-based infrastructures for the future internet: The FIWARE Approach

The remarkable success of cloud computing has change the way services and applications are implemented and offered not least because of the flexibility and scalability that such an environment can offer. Cloud federation has tremendous potential for the industry as an effective way to increase the capacity of resources and diversity of offerings while keeping costs relatively low. Yet cloud federation is still in its infancy, with different approaches being introduced, either in terms of architectural design or the availability of test facilities. This paper introduces the FIWARE approach for federating multiple cloud-based infrastructures targeting different fields related to Future Internet and Smart-Cities innovative developments. Sustainability is considered in the focus of our approach. In addition to the traditional cloud services, the FIWARE federation mainly offers a set of general-purpose platform functions and services that are available through open (public a nd royalty-free), vendor-independent APIs supporting open innovation and extended with further facilities (e.g. sensing and Software-Defined-Networking (SDN) capabilities) advancing the market with smart infrastructures. We present federated infrastructure sustainability considerations along with the FIWARE federation architecture design that is implemented and deployed within a federation of 17 cloud-based infrastructures distributed across Europe